Thermal measurements of a user's face can be useful for various applications such as detection of physiological responses that may manifest through temperature changes to various regions on the face. Some examples of physiological responses include manifestation of emotional responses (e.g., fear and anxiety) or physiological signals (e.g., breathing rate and brain activity). However, collecting such data over time when people are going through their daily activities can be very difficult. Typically, collection of such data involves utilizing thermal cameras that are bulky, expensive and need to be continually pointed at a person's face. Additionally, due to the people's movements in their day-to-day activities, collecting the required measurements often involves performing various complex image analysis procedures, such as procedures involving image registration and face tracking.
Another challenge involve in detecting physiological responses based on thermal measurements of regions on the face involves influence of various confounding factors such as facial movements (e.g., facial expressions, talking, or eating) and the presence of various substances on the face (e.g., makeup, facial hair, or sweat). These confounding factors can change the thermal measurements and lead to errors in the detection of the physiological responses. These confounding factors are often not easily identified from the thermal measurements, and there is a need to be able to collect thermal measurements of various regions of a person's face while accounting for confounding factors.